Compressed LLM Error Compensation Using Low-Rank Matrices
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Solution Overview
Problem
Existing compressed large language models (LLMs) face challenges in empirical deployment due to their considerable size and high inference costs, with most compression methods either degrading accuracy or being constrained by limited hardware-supported formats, lacking flexibility for diverse performance needs.
Innovation Solution
A method to compute an importance score for elements within a compressed LLM and generate a pair of low-rank matrices to compensate for errors, using eigendecomposition and eigenspace projection to allocate representation capacity based on importance scores, allowing for customized error compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compression methods are applied to reduce model size and inference costs, then efficiency and resource usage improve, but accuracy of LLM output deteriorates
Solution Approach 1:
The patent introduces a low-rank matrix as an intermediary component between the compressed LLM and the final output. This low-rank matrix acts as a mediator that compensates for the accuracy loss introduced by compression, allowing the system to maintain both compression efficiency and output accuracy simultaneously
Solution Approach 2:
The patent changes the parameter representation by decomposing the error compensation matrix into low-rank factors. By representing the compensation mechanism in terms of low-rank parameters rather than full-rank matrices, the system maintains accuracy improvement while avoiding the computational overhead of storing and processing large compensation matrices
2Quantity of substance
If compression methods are applied to reduce model size, then device resource requirements decrease, but adaptability to diverse hardware formats is limited
Solution Approach 1:
The patent segments the error compensation mechanism into separate low-rank matrices that can be independently configured. This segmentation allows different parts of the compensation mechanism to be optimized for different hardware formats, enabling the same compressed model to adapt to various hardware requirements while maintaining a compact size
Solution Approach 2:
The low-rank matrix formulation provides a universal representation that can work across multiple hardware formats and compression schemes. By using a format-agnostic error compensation approach, the system achieves multi-functionality where the same compressed model can be deployed on diverse hardware platforms without retraining or format conversion
3Manufacturing precision
If error compensation mechanisms are added to compressed LLM, then output accuracy improves, but computational overhead and latency increase
Solution Approach 1:
The patent transforms the error compensation problem by changing the parameter representation from full-rank to low-rank decomposition. This parameter transformation reduces the computational complexity of the compensation operation from O(n²) to O(nr) where r is the low-rank factor, significantly reducing inference latency while maintaining accuracy benefits
Solution Approach 2:
The patent applies partial error compensation by using low-rank approximation rather than full-rank compensation. This partial action approach captures the most significant error components while deliberately ignoring less important details, achieving a practical balance between accuracy improvement and computational efficiency
Data Source
AI summary
Large language models (LLMs) learn via machine learning to understand and generate human-like text, and thus are power when used for various language-based tasks, such as text summarization, translation, and content generation. However, to provide superior performance, the LLM is often of a considerable model size and requires high inference costs. To mitigate the size and execution costs of LLMs, methods have been developed to specifically compress LLMs. However, most existing methods either incur significant accuracy degradation compared to uncompressed models or have high training time, while their adaptability is often constrained by a limited range of hardware-supported compression formats. The present disclosure provides error compensation for a compressed LLM in a training free manner that provides flexibility for diverse performance needs.